INDEPENDENT RESEARCH STUDY REPORT ON

“To Analyze the Effectiveness of Recruitment Apps among Potential Job Aspirants: A Study

among Students of Selected Colleges in Kerala”

Submitted in Partial Fulfillment of the Requirements for the

Award of the Degree of

Master of Business Administration

Of

Cochin University of Science and Technology, Kochi

By

Ms. Gheethu Maria Joy

Under the Guidance of

Dr. Sam Thomas

Assistant Professor

SCHOOL OF MANAGEMENT STUDIES

COCHIN UNIVERSITY OF SCIENCE AND TECHNOLOGY

MARCH 2016

DECLARATION

I, Gheethu Maria Joy, do hereby declare that this report is a bonafide

record of the Independent Research Study done by me during the academic

year 2015- 2016, in partial fulfillment of the requirements for the award of the

Degree of Master of Business Administration of the Cochin University of

Science and Technology, Kochi, India.

This work has not been under taken or submitted elsewhere in

connection with any other academic course.

Place: Kochi - 22

Date:

Gheethu Maria Joy

ACKNOWLEDGEMENT

First of all I thank Almighty God for his mercy and love which kept me

in good health and sound mind and helped me to complete the independent

research study successfully.

I extend my sincere gratitude to Dr. Sam Thomas, Professor School of

Management Studies - CUSAT, for his expert guidance and assistance.

I express my gratitude to Prof. (Dr.) Moli P. Koshy, Director School of

Management Studies CUSAT, for her guidance and leadership which

motivated and fueled my ambition towards completing this project.

I express my sincere gratitude to all of my respondents. Without them

this research would not have been completed.

I sincerely thank my relatives and friends for their encouragement and

support during the study.

Last but not the least I express my gratitude to everyone who directly

and indirectly helped me for completing the research study in time and thereby

making it a success.

Gheethu Maria Joy

CERTIFICATE

This is to certify that the project titled “To Analyze the Effectiveness of

Recruitment Apps among Potential Job Aspirants: A Study among Students

of Selected Colleges in Kerala” has been prepared by Ms. Gheethu Maria Joy,

School of Management Studies, CUSAT, Kochi – India, in partial fulfillment of

the award of the degree of MBA from Cochin University of Science and

Technology, under my guidance and supervision.

Prof. (Dr.) Moli P. Koshy Dr. Sam Thomas

Director Project Guide

Place: Kochi-22

Date: 22 - 04 - 2016

Table of Contents

Ch.

No.

Title Page

No.

Executive Summary

1

1

Introduction

Introduction to the Study

Problem Statement

Objective of the Study

2

4

4

2

Literature Review

Technology Acceptance Model (TAM)

Perceived Ease of Use (PEOU)

Perceived Usefulness (PU)

Relationship between PEOU and PU

Relationship between Attitude and Intention

Influence of external Variables

Mobile Apply Is Sinking the Recruitment Process

Best Way to Create a Great Mobile Recruiting Experience

6

7

8

9

10

11

12

13

3

Research Methodology and Design

Research Model and Hypotheses

Participants

Sampling Technique

Data Collection Methodology

Data Analysis Methods

15

16

16

16

17

4

Data Analysis and Interpretation

Respondent Demographics

App Usage Rate

Response of those who have used recruitment apps

Response of those who have not used recruitment apps

Reliability and Factor Analysis

Correlation Analysis

Structural Equation Modelling (SEM)

Hypotheses Testing

Influence of gender in Behavioral intension to use of recruitment

apps

Influence of age group in Behavioral intension to use of

recruitment apps

18

21

22

25

31

32

33

38

40

41

5

Limitations

42

6

Findings and Suggestions

43

7

Conclusions

46

8

References

47

9

Appendix

48

1

Executive Summary

Rapid advancements of internet and smart phone has caused made recruitment

process easy. A click may enable professionals to apply to jobs and take part in recruitment

process they were waiting for. Instant notifications could also be received via use of apps

related to vacancies and recent job openings. Since people often carry their phones with them

and a very few professionals are without smartphones nowadays, it has been easy for

recruiters to find a best fit candidate for their job as well as professionals to take part in the

recruitment process of their dream company. This study examines how effective users feel

about recruitment through apps and the extent to which this has been made a reality.

This study also gets into the problems users face with regard to recruitment through

apps and invites suggestion’s from users to improve the company’s recruitment app. User’s

acceptance of the technology of recruitment through apps has been analysed with the help of

TAM (Technology Acceptance Model). The study also enquires the reasons as to why there

has been reluctance towards recruitment through apps and how the non - user group be

attracted more towards recruitment process via apps.

2

Chapter 1: Introduction

1.1 Introduction to the Study

Apps are evolving the recruitment process, and along with it, the job apply.

Today, job seekers expect to view jobs seamlessly on their mobile devices and apply

to them quickly and easily. So have all companies' career sites jumped on the mobile-

friendly bandwagon? Not by a long shot. “There are all kinds of excuses why

companies aren’t doing mobile recruiting, but it’s a must to remain competitive,” says

Michael Marlatt, executive recruiting partner at Cognizant Technology Solutions and

founder of MREC, an annual mobile recruiting conference.

Job seekers are voting with their mobiles. According to a Pew Internet study,

43 percent of smartphone owners used their mobile to look up information about jobs.

The study points to an even more urgent message for employers that have taken it

slow with mobile: 18 percent of smartphone users submitted a job apply via mobile.

Adding to the urgency, Google announced that, beginning in late April 2015,

they will penalize web sites that fail the search giant's mobile responsive test. The

result will downgrade these sites' placement in search results served to mobile

devices. “Some estimates say 81 percent of company sites will be affected by

Google’s new algorithm,” says Marlatt, “If you care about your ranking, and want to

be seen as innovative, you want to be mobile-ready."

So if your company hasn't revamped its mobile recruiting process recently, it's time.

Here are some pointers.

Complexity kills candidate interest. Pare down your career site to bare

essentials. “Many job seekers fail to complete the job apply because of its

complexity,” says Amy Hughes, director of client adoption at Monster. “An average

of 60 to 70 percent of seekers applying to Monster jobs through company career sites

will never complete the apply process.”

Assess candidates’ priorities. Ask recent hires which elements of your career

site attracted them and which didn't. “Think about what’s going to be important to

users,”

3

says Marlatt. “Make the things that most people want as easy as possible to find. You

need to minimize fluff.”

In 2015, mobile job search is an expected convenience. Cater to the lifestyles

of your ideal candidates. “If I’m waiting to pick up my daughter from a music lesson

and I see all these jobs on my smartphone, I want to be able to apply from my phone,”

says Barry Wu, a senior product manager at Monster.

Test-drive your career site on your phone. Play the part of the mobile seeker.

“The first thing companies need to do is assess their sites’ mobile friendliness,” says

Marlatt. “Go and navigate the career site from a mobile device, a smartphone or tablet.

How does it render? What does the candidate experience from the first point of entry?

What’s it like to navigate?”

Ensure that your site is mobile-responsive. Put your site through Google's test for

mobile-responsiveness. “If you realize you’re not mobile friendly, you need to

consider a mobile-responsive design,” says Marlatt. “It may be too much for IBM to

rebuild their site from the ground up for mobile – that’s a huge undertaking. The

workaround is to have a redirect to a simplified mobile site.”

The best sites respond to the mobile device in the user's hand. After you've

made your career site at least minimally mobile-responsive, refine it for all the devices

used by your target candidates. “Apply with Monster, a one-button apply solution, is

smart enough to determine what’s the best experience for the particular mobile device

that the user is on," says Wu.

Consider the nothing-but-mobile apply. Can you decide which applicants to screen

with a phone interview, based solely on their input via mobile? Make this your next

goal. “People are doing quick searches on their phones, just to see what jobs are new

since yesterday,” says Wu. “If you put the whole apply on the phone, candidates

won’t be forced to set a task to complete the process on a laptop or desktop."

On the flip side, avoid trying to fully vet the candidate from her mobile

apply. Don't front-load your application workflow with processes that can wait.

4

“Details like background checks often can be covered during the interview process,”

says Hughes.

If you neglect mobile, you may miss out on diversity. Finally, keep in mind

that if your career site turns off mobile users, your workforce demographics may

suffer. “Hispanics, African-Americans and women are the heaviest users of mobile

technology," says Marlatt. "So when you think about diversity recruiting, mobile is

key.”

1.2 Problem Statement

Nowadays there are apps for almost everything. Understanding the benefits of apps

and its usage rate companies have introduced apps in recruitment procedure, in order

to identify potential candidates those can best fit their job profile. This study aims in

finding out user’s perspective about the recruitment process via apps.

1.3 Objective of the Study

1. To determine the effectiveness of recruitment apps in recruitment process.

2. To determine the usage rate of recruitment apps.

3. To determine reasons as to why there are reluctances, if any, among users to

the recruitment process via apps

4. To find out whether investing in apps would be beneficial for the company

with respect to finding out the best fit candidate for their company.

5

Chapter 2: Literature Review

In 1989, Davis (1989) proposed the technology acceptance model (TAM) to

explain and predict user’s behaviour to use a technological innovation, specifically in

user acceptance of an information technology. TAM is originally an extension of

Theory Reasoned Action (TRA) (Fishbein and Ajzen, 1975), a psychological theory

that seeks to explain people’s actions by identifying the causal connections between

various components: beliefs, attitudes, intentions and behaviours. Unlike the TRA,

TAM is built on two primary variables —- independent variables which includes

perceived usefulness (PU) and Perceived ease-of-use (PEOU) and the dependent

variable Attitude towards using (AT). Davis (1989) defined perceived usefulness as

―the degree to which a person believes that using a particular system would enhance

his or her job performance,‖ and defined perceived ease of use as, ―the degree to

which a person believes that using a particular system would be free of effort.‖

Furthermore, Davis (1993) theorized that actual information system usage is

determined by behavioural intention, and the intention is jointly determined by the

users’ attitude toward using the system and perceived usefulness.

In previous studies, TAM has been widely used by information technology

researchers to gain a better understanding of information technology(IT) adoption and

its usage in organisations (Chismar and Wiley-Patton,2002). However, Legris et al.

(2003) argued that it is imperfect as all TAM relationships are not borne out in all

studies - there remains a wide variation in the predicted effects in various studies with

different types of users and systems. This study by using Technology Acceptance

Model (TAM) aims to investigate issues related to perceptions, intentions and

attitudes towards using apps for recruitment purpose.

6

2.1 Technology Acceptance Model (TAM)

Based on established pertinent theoretical foundations and literature, a research

model has been employed to investigate the technology acceptance factors that

influence the adoption of recruitment apps. The TAM (Davis, 1989), with its basis in

the TRA (Ajzen, 1991; Fishbein & Ajzen, 1975) has emerged as a powerful model in

investigating the acceptance and use of information technology; its relationship to

TRA has been discussed extensively in the literature (Davis, 1989; Keil, et al., 1995;

Mitchell & Greatorex, 1993; Roger, 1995) and need not be elaborated here. The

choice for using TAM as a research model to explain respondents recruitment app

adoption is attributed to its consistent capability to explain a substantial portion of

variances between behavioural intention and actual behaviours derived mainly from

research into the purchase of technology related products (Bobbit & Dabholkar, 2001;

Goldsmith, 2002; Grabner-Krauter & Kaluscha, 2003; Hanque, et al., 2006; King &

He, 2006). The TAM postulates that the perceptions or beliefs about the innovation

are instrumental in the development of attitudes that will eventually result in system

utilization behaviour (Davis, 1989). It also posits that the actual system use is

determined by each user’s behavioural intention to use, which is in turn influenced by

each user’s attitudes towards use. Finally, the attitude is directly affected by the

usefulness and ease of use of the system.

Although this model was originally conceived to model the adoption of

information systems in the workplace (Davis, 1989), scholars in the area of consumer

behaviour have identified two specific dimensions which are relevant to use of

recruitment apps: perceived ease of use and perceived usefulness (Gefen, et al., 2003;

Huang, 2008).

7

2.2 Perceived Ease of Use (PEOU)

According to TAM, PEOU is a major determinant that affects acceptance of a

particular technology (Davis, et al., 1989). PEOU is defined as the concentration of

physical and mental efforts that a user expects to receive when considering the use of

technology i.e. the degree to which a particular technological system would be free

from effort (Davis, 1989). According to Buton-Jones and Hubona (2005), the ease of

learning and becoming skilful at using pervasive technologies, including technologies

and interfaces on online shopping sites, were concluded as valid determinants as to

what makes a technology easy to use. The work of Selamat et al. (2009) further added

that a technology which is perceived to be easier to use than another is more likely to

be accepted by users whereas the more complex a technology is perceived to be, the

slower will be its rate of adoption. This is supported by Teo (2001) as the study

concluded that a system which is easy to use often requires less effort on the part of

users and thereby increases the likelihood of adoption and usage of a particular

technology. Other scholars have also found that the perceived ease of use had a

positive influence on consumers’ attitude in using the Internet for recruitment purpose

(Bisdee, 2007; Yulihasri & Daud, 2011). This is consistent with the work of Childers

et al. (2001) which argued that online recruiters which are able to provide recruitment

apps which are clear and understandable, with less mental effort requirement, and

allow professional’s to use recruitment apps the way they want results in ease of use

perceptions in professional’s minds with favourable attitudinal attachment to online

recruiters which are able to do so.

Accordingly, the study proposes that:

―Perceived ease of use positively affects attitudes towards recruitment apps.‖

8

2.3 Perceived Usefulness (PU)

PU is another major determinant of attitude toward use in the TAM model

(Davis, et al., 1989). PU is defined as the degree to which the user believes that the

technology will enhance the performance of an activity (Davis, 1989). According to

McCloskey (2004), the ability to improve recruitment apps performance, recruitment

apps productivity, and most importantly, accomplishing recruitment apps goals, were

concluded as valid determinants as to what makes professional’s recruitment activity a

success. This is in line with the findings of Barkhi et al. (2008) as their study suggests

that professional’s will develop favourable attitudes toward recruitment apps that they

believe to provide sufficient benefits or attributes toward a solution and negative

attitudes toward those that are inadequate. Given this scenario, Kim et al. (2003)

argued that recruitment apps which provide functions which aid professional’s in

making better decisions will be perceived as useful. The same logic was observed in

the work of Bisdee (2007) as recruitment apps which are able to provide useful

services to professional’s and services which are not available through traditional

recruitment methods will be perceived as useful by consumers, and thus leads to the

development of favourable attitudes toward recruitment apps. This notion is supported

by Childers et al. (2001) whereby their findings suggest that professionals which had

favourable attitudes toward recruitment apps were found to perceive recruiters as

being useful as recruiters were able to enhance their recruitment apps productivity,

effectiveness and ability.

Hence, it is expected that:

―Perceived usefulness positively affects attitudes towards using recruitment apps.‖

9

2.4 Relationship between PEOU and PU

Perceived ease of use has been empirically verified by many studies as a

predictor of perceived usefulness (King & He, 2006). When all other factors are equal,

users are likely to consider a technology useful when they perceive it as easy to use

(Bruner & Kumar, 2005). Nonetheless, the relationship remains contradictory

(Aladwani, 2002). The work of Gefen and Straub (1997) concluded that the

relationship was not significant in predicting e-mail acceptance as a technology, while

more recent researches (e.g. Jantan, et al., 2001; Shyu &

Huang, 2011) proved otherwise. However, in existing studies around the area of

electronic commerce, both are surmised to be closely linked as Ramayah and Ignatius

(2005) argued that consumers who perceive that recruitment apps is effortless should

in turn develop a tendency to perceive it as useful. The rationale behind such a

phenomenon is due to the fact that consumers would inherently try to mould his or her

perception of recruitment apps based on his or her own experiences in engaging in

recruitment process via apps and the ease in which the shopping activity was

executed. This is in line with the work of Heijden (2000) which suggests that the

easier it is for professional’s to use recruitment apps, the more useful recruitment apps

will be perceived by professional’s.

Therefore, this study anticipates that:

―Perceived ease of use positively affects perceived usefulness of recruitment apps.‖

10

2.5 Relationship between Attitude and Intention

Attitude has long been identified as a cause of intention (Suki & Ramayah,

2010). Fishbein and Ajzen (1975) have categorized attitude into two distinctive

constructs: the first refers to attitude toward the object while the second refers to

attitude toward the behaviour. While the former is referred to as a person’s evaluation

of a specified object, the latter points to a person’s evaluation of a specified behaviour.

Against this backdrop, the latter categorization of attitude is adapted as the

conceptualized operationalization of attitude used in the TAM model - in which in the

adapted form refers to the mediating affective response between usefulness and ease

of use beliefs and intentions to use a target system (Suki & Ramayah, 2010). In the

context of recruitment via apps, the apps for a recruiter is the main contact point by

which the recruiter and professional’s interface in the recruitment process. (Ahn, et al.,

2004). Past scholars have emphasised the understanding of user expectations and how

they feel about the websites that they use (Chen, et al., 2002; Suh & Han, 2002). In the

work of Al-Rafee and Cronan (2006), attitude was found to be the most significant

construct in influencing behavioural intention. Notably, the more positive a

professional’s attitude towards recruitment apps, the higher the intention he or she has

to engage in recruitment via apps (Ahn, et al., 2004).

Thus, the study posits that:

―Attitude towards using positively affects behavioural intention to use recruitment

apps‖

11

2.6 Influence of External Variables

External variables also influence TAM model. For the purpose of this study two

external variables were considered – Gender and Age. This was done to determine if gender

and age had an influence on behaviral intension to use of recruitment apps. Based on this two

hypothesis statements were made :

―Gender had an impact on Behavioral intention towards use of recruitment apps‖

―Age group had an impact on Behavioral intention towards use of recruitment apps‖

12

2.7 Mobile Recruitment Application Process

The number one shortcoming for most career sites is the lack of a mobile

experience altogether. In fact, according to a study done by iMomentus, just 10% of

the Fortune 500 have a mobile optimized web site for their career section. Based on

our own tracking of career sites, 25% of the traffic our clients get comes from mobile.

That means most of the Fortune 500 has a substandard, and in many cases,

dysfunctional application process on mobile.

Here’s what a typical application process looks like from the perspective of a

candidate on his mobile phone:

1. The candidate enters your career site on his mobile phone. You might’ve paid

for this traffic from a source like Indeed, or he may have just entered from your

corporate site. The candidate could’ve also entered your site from a job alert he

got by email from you. Guess how most people first read their emails

afterhours? That’s right, on their phone.

2. If you’re like 90% of the Fortune 500, you don’t have a mobile optimized

career site and instead the candidate sees a large, hard to navigate, and

cumbersome job search.

3. Once the candidate finds a job that’s a good match he wants to apply. Your

career site asks the candidate to upload a resume and fill out dozens of fields.

The applicants gets frustrated and leaves.

This is a huge opportunity lost for the company to recruit a potentially qualified

candidate. Worst of all, it’s a problem that’s not addressed on the career sites of many

tech companies.

13

2.8 Stages in Mobile Recruiting Process

The best way to create a great mobile recruiting experience for the candidates is to

look through your entire recruiting process and optimize every single touch point with

the candidate.

There are three stages in recruiting process:

1. Learn:

This is the part where a prospect visits your company and careers page, reads

about your culture, looks at job opportunities. To make this part of the experience

mobile-friendly, you should most likely implement responsive web design for your

website (meaning that the website will adapt to any screen size automatically), use a

lot of visuals, and little text. Make sure that your company looks equally good on any

screen size, regardless of whether it’s desktop, tablet or phone. Avoid using Flash

since it’s not supported on certain mobile devices.

2. Apply:

Here the candidate selects an opportunity and submits their application. Most

people cannot store resumes on their mobile devices nor do they have the patience to

fill out 50 questions on a tiny phone screen. To solve for this, make sure that

candidates can apply with their social profile or just a basic set of information. The

whole processes should take no longer than a minute or two. If you manage to nail this

part of the strategy, your candidates will most likely will be very impressed with how

seamless your recruiting process is.

14

3. Engage:

This is how you communicate with the candidate once they are in your system.

Typically the bulk of the communication is through email, so make sure your

messages are easy to read on a small screen. Use text sparingly and make sure

everything you say has a clear call to action. You can infuse mobile into the candidate

interview experience as well. At LinkedIn we text them with directions and traffic

alerts and them with an iPad pre-loaded their interview schedule for the day. Think

about what might make sense for your company.

15

Chapter 3: Research Methodology and Design

Data was collected from two different groups: users and non – users of

recruitment apps. Research model was applied to user group. Those in the user group

are examined based on their current use of recruitment apps or their previous

experience of usage. TAM has successfully predicted and explained the behavioural

intention to adopt the technology. The following research model was adopted.

The following hypothesis’s with reference to TAM model was tested:

(i) Perceived ease of use positively affects perceived usefulness of recruitment

apps.

(ii) Perceived ease of use positively affects attitudes towards recruitment apps.

(iii) Perceived usefulness positively affects attitudes towards using recruitment

apps.

3.1 Research Model and Hypotheses

Perceived

Usefulness

Perceived

Ease of Usage

Attitude

towards Use

Behavioural

Intension to

Use

-- Research Model

16

(iv) Attitude towards using positively affects behavioural intention to use

recruitment apps

(v) Perceived ease of use positively affects intention to use mobile recruitment

apps.

(vi) Perceived usefulness positively affects intention to use recruitment apps.

Participants of this study consisted of:

(i) Job aspirants of Kerala who have completed or is in the verge of completion

of degree and PG courses (Btech/ BCA/ BCom/ BBA/ MBA/ Mtech) from

universities of CUSAT, Mahatma Gandhi and Kerala (both Private and

Government colleges),

(ii) Currently working Professionals who were in search for a job in their dream

company.

While it is difficult to get responses from a whole population, sampling is an

attempt to draw a conclusion based on a small representation in a given population.

The sampling technique used in the present study is non-probability convenience

sampling. Convenience sampling is found used in many studies investigating

technology acceptance. Further, the technique is used to ensure a better response rate

in a short amount of time.

The data collection was done by survey research method using questionnaire

distributed online via google forms. Responses were collected from 100 users and 200

non – users of recruitment apps. Telephonic interview was also done to get reliable

responses

3.2 Population

3.4 Data Collection Methodology

3.3 Sampling Technique

17

Factor analysis and reliability testing were performed to ensure that all research

constructs are reliable. Factor analysis was executed in order to reduce the complexity

of research constructs, as well as ensuring that minimal latent constructs were

employed to explain the shared variance of measured constructs use in the

questionnaire. Reliability analysis was conducted in order to check the internal

validity and consistency of the items. Hypothesis testing is performed to determine

whether hypotheses proposed based upon a review from existing literature are

supported or not supported. Independent sample t-Test was conducted to determine

whether gender had an influence in behavioural intension to use of recruitment apps.

One way Anova was performed to determine whether age group had an influence in

behavioural intension to use of recruitment apps. Statistical Package for Social

Sciences (SPSS) software was used to perform all the above analysis. Structural

Equation Modelling (SEM) was also performed to determine whether the TAM model

is valid in this study. Warppls was the software used in performing SEM.

3.5 Data Analysis Methods

18

Chapter 4: Data Analysis and Interpretation

4.1 Respondent Demographics

This questionnaire was administered to 300 respondents via google forms.

Respondent consisted of 55% female and 45% male; age in the range of below 20

years (3.3%), 20 to 24 years (69.2%), 25 to 30 years (9.2%) And above 30 years

(0.8%) ; with bachelor’s degree in technology (68.3%), in computer applications

(12%), in science with computer (5%), in science with maths/ physics / chemistry

(6%), in commerce (5%) and in others like multimedia (3%).

Gender Distribution

[CATEGORY NAME]

[PERCENTAGE]

[CATEGORY NAME]

[PERCENTAGE]

Male

Female

19

Age Distribution

Bachelor’s Degree

0.8 3.3

9.2

69.2

0

10

20

30

40

50

60

70

80

Above 30 years Below 20 years 25 to 30 years 20 to 24 years

Perc

en

tag

e

Age

68.3

12

11

5

3

0 10 20 30 40 50 60 70 80

Btech

BCA

BSc

Bcom

Others

Percentage

Deg

ree

20

The table below summarizes the demographic information of the respondents.

Sl.No. Socio-demographic

variables

Description Percentage

(%)

1. Gender Male

Female

45

55

2. Age Below 20 years

20 to 24 years

25 to 30 years

Above 30 years

3.3

69.2

9.2

0.8

3. Under Graduation Btech

BCA

BSC.

Bcom

Others

68.3

12

11

5

3

21

4.2 App Usage Rate

Out of the total respondents 33% of them had used recruitment apps and 66%

of them had not used recruitment apps. This means that only a few of the respondents

have adopted to the technology of recruitment apps and there is a high level of

reluctances with respect to use of recruitment apps.

Used 33%

Not Used 66%

Used recruitment apps

Not used recruitment apps

22

4.3 Response of those who have used recruitment apps

This portion of the questionnaire aimed at identifying the problems respondents

faced while using recruitment apps. Suggestions to improve apps were also recorded.

a. How they had come to know about the company’s recruitment app

Majority of the respondents (58.8 %) had come to know about recruitment apps

via friends. 23.5% became aware of recruitment apps while simply searching through

app store, 11.8 % became aware through many other sources like google search for

websites. 5.9% of them became aware about apps from their family / relatives.

b. Reasons for downloading the company’s recruitment app?

82% of the students were interested in recruitment app mainly because they

were able to know about the latest job opening through pop ups and notifications

quickly and this way they wouldn’t miss out an opportunity. Among this 82%, 70% of

the students were unemployed, 11 % wanted to be part of their dream company. 8%

who used apps had jobs for themselves but were trying to get updated with job

openings to help their friends know about it.11% of the students felt they could easily

58.8

23.5

11.8

5.9 0

10

20

30

40

50

60

70

Friends While simply searchingthrough app store

Other Family / Relatives

23

know more about the company from apps.10% used apps because they found it easy

and quick to track their application status.7% of the sample were found to be app

developers and there were another 12% who were also interested to know about the

various features the apps offered. 23% of the students wanted to see how apps could

be useful and they were just in the verge of trying it and had not much experience to

share. 17% of the student were using apps because they felt apps were simple, easy to

use and user friendly. There were 10% of students who used apps just because their

friends found them useful.

c. Percentage of students still using the app?

59% of the 33% respondents who used recruitment apps had discontinued its

usage whereas about 41% still used apps.

d. Name of the apps being used for recruitment purposes

Students were found using the following apps:

* 35% of the students used TCSNextStep app,

* 60% each used the app of naukri and indeed jobs,

* 5% each used apps Jobscience, Linkedin Recruiter, Monster. Com,

freshersworld, shine, Pocket aptitude, M4maths, Reliance.

[CATEGORY NAME]

[PERCENTAGE]

[CATEGORY NAME]

[PERCENTAGE]

Continued using apps

Apps Discontinued usage of Apps

24

e. Reason as to why professionals have not applied to a job via mobile

Device (User’s point of view)

Majority of the respondents (41.2%) felt complexity and inability to customize

resume as the main reason as to why professionals have not applied to a job via

mobile device. 29.4 % of them had not been looking for it. 17.6 % felt the main

reluctance towards apps could be the inability of apps to store resume and the amount

of time consumption which was often too long than other means. 5.29% of them

needed more time to adjust with the use of apps. 5.90% couldn’t find any mobile

application availability. None of them felt uninteresting job could be reason for

reluctance for using recruitment apps.

0 5.90% 5.29%

17.6 17.6

29.4

41.2 41.2

0

5

10

15

20

25

30

35

40

45

Nointeresting

job

No mobileapplyoption

Neededmore timeto adjustwith theuse ofapps

Tried buttook too

long

No resumestored

Haven’t been

looking

Tried buttoo

complex

Couldn’t customize

resume

25

4.4 Response of those who have not used recruitment apps

This portion of the questionnaire aimed at identifying the reasons as to why

there were reluctance in the sample population from using recruitment apps and how

many of them could be made to use these apps.

a. Possess Smartphones

Among the 66% of the respondents who did not use recruitment apps, 96.1% of

them possessed smartphones.

b. Frequency of using internet in phones

[VALUE]

0

20

40

60

80

100

120

YES

26

Majority of the respondents (79.6 %) who had never used recruitment apps

where using internet in smartphones several times a day.

c. Purpose of Using apps

0 0

1

3.9 3.9

11.7

0

2

4

6

8

10

12

14

Only 1 to 2days a week

Only 1 to 3times a month

No, I havenever used

internet in myphone

3 to 5 days aweek

Only when Ifeel its

absolutelynecessary

1 to 2 times aday

77.5

42.2

88.2

81.4

89.2

5.9

0

10

20

30

40

50

60

70

80

90

100

Reading News Job Search Entertainment Social media Watsapp Other

27

89.2% of the respondents used watsapp. 81.4% were using apps for social

media engagement and 88.2 were using it for other entertainment purposes. 77.5%

were using apps to read news. 42.2 % used apps for job search and 5.9 % used apps

for other purposes like playing games.

d. Apps Role in Job search

80% of respondents who never used recruitment apps believed that apps could

definitely help in job search while 20% of them felt apps could never be helpful with

regard to search of job opportunities.

[CATEGORY NAME]

[PERCENTAGE]

[CATEGORY NAME]

[PERCENTAGE]

ROLE IN JOB SEARCHES

YES

No

28

e. Response to statement:

Statement 1:

“I have stopped applying if their mobile technology isn’t up to par. That

alone tells you something about the company’s priorities and whether

they are savvy.”

7.8

15.5

53.4

18.4

4.9

0

10

20

30

40

50

60

StronglyDisagree

Disagree Neutral Agree Strongly Agree

29

4.9% of the respondents strongly believed that a company should be able to

show its mettle in mobile applications. 18.4 % just agreed to the statement. 53.4 %

were in a neutral opinion meaning its fine both ways. 7.8% strongly disagreed to the

statement and 15.5 % could not support the statement.

Statement 2:

“I haven’t downloaded my dream company’s career app on my mobile

device. However, I have visited my dream company’s career site. Save

time and money and invest in your site instead of building a complicated

app”

8.7

23.3

33

30.1

4.9

0

5

10

15

20

25

30

35

Strongly Disagree Disagree Neutral Agree Strongly Agree

30

4.9% of the respondents strongly believed that a company should not waste its

time building complicated apps. 30.1 % just supported the statement. 33 % were in a

neutral opinion meaning it is fine both ways. 23.3% could not support the statement

and 8.7 % totally disagreed believing apps were not waste of time and money.

f. Expectations from a company’s recruitment app

88.2 % of respondents wanted recruitment apps to give them job alerts. 80.4 %

felt they should be able to apply for jobs via recruitment apps. 76.5 % wanted to these

apps to enable them to track their application status. 58.8 % felt they should be able to

share information via email also. 36.3 % felt recruitment apps should enable them to

share information via social networks as well. 2% wanted other features such as

history of the company and recent developments in the company as well.

31

4.5 Reliability and Factor Analysis

Factor analysis and reliability testing were performed to ensure that all research

constructs are reliable. Factor analysis is a statistical method used to

describe variability among observed, correlated variables in terms of a potentially

lower number of unobserved variables called factors. The relationship of each variable

to the underlying factor is expressed by factor loadings. Reliability analysis was

conducted in order to check the internal validity and consistency of the items used for

each factors. Cronbach’s alpha value from reliability test was utilized. A common rule

88.2

76.5

80.4

58.8

36.3

2

0

10

20

30

40

50

60

70

80

90

100

Should be ableto receive job

alerts

Should be ableto track

applicationstatus

Should be ableto apply for

jobs

Should be ableto share

information viaemail

Should be ableto share

informationthrough social

networks

Other

32

of thumb is that indicators should have a Cronbach’s alpha of at least 0.7 to judge the

set of items as reliable. The analysis was done using SPSS software and the result of

the analysis is summarized in the table below.

Four factors were: perceived usefulness, perceived ease of use, attitude toward

use of recruitment apps, and Behavioral intention to use recruitment apps

Factors loading ranged from 0.754 to 0.980, Cronbach’s alpha was calculated

for each factor (0.755, 0.866, 0.767, and 0.980) and each was found reliable.

Dimension Item Factor

Loadings

Cronbach’s

alpha

Perceived ease of use PEU1

PEU2

PEU3

PEU4

.832

.926

.887

.754

.755

Perceived usefulness PU1

PU2

.884

.884

.866

Attitude toward use ATU1

ATU2

.812

.812

.767

Behavioural Intention to use BIU1

BIU2

.980

.980

.980

4.6 Correlation Analysis

Correlation analysis was performed to examine the relationship between

perceived ease of use, perceived usefulness, attitude toward recruitment apps, and

intention to use recruitment apps. The bivariate relationships indicated that all of the

variables were significantly correlated with each other at the 0.01 level. The

correlations among the latent constructs ranged from .191 to .885. The results support

the non-existence of multi-collinearity as all correlation values are below 0.7, so no

multi-collinearity was found among the latent variables.

33

Factor PU PEU ATU BIU

PU Pearson

Correlation 1 .660** .502** .492**

PEU Pearson

Correlation .660** 1 .519** .637**

ATU Pearson

Correlation .502** .519** 1 .507**

BIU Pearson

Correlation .492** .637** .507** 1

**. Correlation is significant at the 0.01 level (2-tailed).

4.7 Structural Equation Modelling (SEM)

Structural equation modeling (SEM) refers to a diverse set of mathematical

models, computer algorithms, and statistical methods that fit networks of constructs to

data. SEM includes confirmatory factor analysis, path analysis, partial least squares

path analysis, LISREL and latent growth modeling. The term should not be confused

with Structural Modeling in economics. Structural equation models are often used to

assess unobservable 'latent' constructs. They often invoke a measurement model that

defines latent variables using one or more observed variables, and a structural model

34

that imputes relationships between latent variables. The links between constructs of a

structural equation model may be estimated with independent regression equations or

through more involved approaches such as those employed in LISREL.

Use of SEM is commonly justified in the social sciences because of its ability

to impute relationships between unobserved constructs (latent variables) from

observable variables. To provide a simple example, the concept of human

intelligence cannot be measured directly as one could measure height or weight.

Instead, psychologists develop theories of intelligence and write measurement

instruments with items (questions) designed to measure intelligence according to their

theory.[6] They would then use SEM to test their theory using data gathered from

people who took their intelligence test. With SEM, "intelligence" would be the latent

variable and the test items would be the observed variables.

In SEM diagrams, latent variables are commonly shown as ovals and observed

variables as rectangles. The below diagram shows how error (e) influences each

intelligence question and the SAT, ACT, and GPA scores, but does not influence the

latent variables. SEM provides numerical estimates for each of the parameters

(arrows) in the model to indicate the strength of the relationships. Thus, in addition to

testing the overall theory, SEM therefore allows the researcher to diagnose which

observed variables are good indicators of the latent variables.

Various methods in structural equation modeling have been used in the

sciences, business, education, and other fields. Use of SEM methods in analysis is

controversial because SEM methods generally lack widely accepted goodness-of-fit

statistics and

Most SEM software offers little latitude for error analysis. This puts SEM at a

disadvantage with respect to systems of regression equation methods, though the latter

are limited in their ability to fit unobserved 'latent' constructs.

35

WarpPLS is a powerful PLS-based structural equation modeling (SEM)

software. Since its first release in 2009, its user base has grown steadily, now

comprising more than 5,000 users in over 33 countries.

Some of its most distinguishing features are the following:

- It is very easy to use, with a step-by-step user interface guide.

- It identifies nonlinear relationships, and estimates path coefficients accordingly.

- It also models linear relationships, using standard PLS algorithms.

- It models reflective and formative variables, as well as moderating effects.

- It calculates P values, model fit indices, and collinearity estimates.

This study was intended to simultaneously examine the direct and indirect

relationships among the constructs of the proposed model and to test the fit between

the proposed model and the obtained data. For its ease and wide applicability in

modelling multivariate relations, SEM with WarpPls was chosen to do the analyses.

From the results of SEM it is clear that Attitude towards use of recruitment apps is

more influenced by users perceived usefulness (β = 0.41) than their Perceived ease of

usage ( β = 0.24). Behavioural Intension to use of recruitment apps is most influenced

36

by Attitude towards use (β = 0.40), followed by perceived ease of usage (β = 0.30)

and Perceived usefulness (β = 0.24).

Model fit and quality indices in SEM

Parameter Test

value

Standard Conclusions

Average path coefficient

(APC)

0.497 P<0.001 Significant

Average R-squared (ARS) 0.422 P<0.001 Significant

Average adjusted R-squared

(AARS)

0.414 P<0.001 Significant

Average block VIF (AVIF) 2.044 acceptable

if <= 5,

ideally <= 3.3

Ideal

Average full collinearity VIF

(AFVIF)

1.925 acceptable

if <= 5,

ideally <= 3.3

Ideal

Tenenhaus GoF (GoF) 0.586 small >= 0.1,

medium>=0.25,

large >= 0.36

large

Sympson's paradox ratio

(SPR)

1.000 acceptable

if >= 0.7,

ideally = 1

Ideal

R-squared contribution ratio

(RSCR)

1.000 Acceptable

if >= 0.9,

ideally = 1

Ideal

Statistical suppression ratio

(SSR)

1.000 acceptable

if >= 0.7

Acceptable

Nonlinear bivariate causality 1.000 acceptable Acceptable

37

direction ratio (NLBCDR) if >= 0.7

Regarding the meaning and calculation of fit indices, their names are

descriptive and were calculated as averages of absolute-value path coefficients (APC),

R-squared values (ARS), average adjusted (i.e., divided by degrees of freedom) R-

squared (AARS), and average variance inflation factors (AVIF). The Tennenhaus GoF

index (Tenenhaus et al. 2005) is a measure of a model’s explanatory power, defined as

the square root of the product between the average communality index and ARS. The

communality index for a latent variable is defined as the sum of squared, un-rotated

loadings with each loading associated with an indicator, divided by the number of

indicators. The average communality index for a model is defined similarly, and

considers all latent variables in the calculation. The SPR index is a measure of the

extent to which a model is free from Simpson’s paradox instances. A Simpson’s

paradox indicates a causality problem, suggesting that a hypothesized path is either

implausible or reversed (Pearl 2009; Wagner 1982). The RSCR index is a measure of

the extent to which a model is free from negative R-squared contributions, which

occur with Simpson’s paradox instances (Pearl 2009; Wagner 1982). The SSR index

is a measure of the extent to which a model is free from statistical suppression

instances (MacKinnon et al. 2000). A statistical suppression instance indicates a

causality problem, suggesting a hypothesized path is either implausible or reversed.

The NLBCDR index is a measure

of the extent to which bivariate, non-linear coefficients of association support the

hypothesized directions of causal links in a model.

38

4.8 Hypotheses Testing

Hypothesis testing is performed to determine whether hypotheses proposed

based upon a review from existing literature are supported or not supported. The

following hypothesises were tested based on the research framework.

H1: Perceived ease of use positively affects perceived usefulness of recruitment apps.

39

H2: Perceived ease of use positively affects attitudes towards recruitment apps.

H3: Perceived usefulness positively affects attitudes towards using recruitment apps.

H4: Attitude towards using positively affects behavioural intention to use recruitment

apps

H5: Perceived ease of use positively affects intention to use mobile recruitment apps.

H6: Perceived usefulness positively affects intention to use recruitment apps.

Hypothesis

P value

Beta

value

Supported

or

Not

supported

H1: Perceived ease of use

positively affects perceived

usefulness of recruitment apps.

0.00 0.67 Supported

H2: Perceived ease of use

positively affects attitudes towards

recruitment apps.

0.00 0.24 Supported

H3: Perceived usefulness positively

affects attitudes towards using

recruitment apps.

0.00 0.41 Supported

H4: Attitude towards using

positively affects behavioural

intention to use recruitment apps

0.00 0.40 Supported

H5: Perceived ease of use

positively affects intention to use

mobile recruitment apps.

0.00 0.30 Supported

H6: Perceived usefulness positively

affects intention to use recruitment

apps.

0.00 0.24 Supported

40

H1 to H6 was verified using SEM. From the results of SEM it is clear that Attitude

towards use of recruitment apps is more influenced by users perceived usefulness (β =

0.41) than their Perceived ease of usage ( β = 0.24). Behavioural Intension to use of

recruitment apps is most influenced by Attitude towards use (β = 0.40), followed by

perceived ease of usage (β = 0.30) and Perceived usefulness (β = 0.24).

.

4.9 Influence of gender in Behavioral intension to use of

recruitment

apps

The variables were grouped based on gender. The aim of this test was to determine

how much impact gender had on Behavioural intension to use of recruitment apps.

The results were as follows.

Group Statistics

Gender N Mean Std. Deviation Std. Error Mean

BI Male 46 3.32 .805 .119

41

Female 55 3.05 .762 .103

Independent Samples Test

Levene's Test

for Equality of

Variances

t-test for Equality of Means

F Sig. t df Sig. (2-

tailed)

Mean

Difference

Std. Error

Difference

95% Confidence

Interval of the

Difference

Lower Upper

BI

Equal

variances

assumed

1.853 .177 1.669 99 .098 .261 .156 -.049 .571

Equal

variances

not assumed

1.660 93.788 .100 .261 .157 -.051 .572

Since Significance value is greater than 0.01, this implied gender had no effect

on Behavioural intention to use of recruitment apps.

Thus the hypothesis based on literature review which stated “ Gender had an

impact on Behavioral intention towards use of recruitment apps” is not supported.

4.10 Influence of Age group in Behavioral intension to use of

recruitment apps

One way anova was the test performed to determine if age group had impact on

Behavioral intention to use of recruitment apps.

The age groups considered were

(i) Below 20 years, (ii) 20 to 24 years, (iii) 25 to 30 years, (iv) Above 30 years.

42

The following results were obtained:

ANOVA

BI

Sum of Squares df Mean Square F Sig.

Between Groups .445 2 .222 .354 .702

Within Groups 65.914 105 .628

Total 66.359 107

Since the value of significance was greater the 0.01, this implied Age group did

not have an impact on Behavioural intension to use of recruitment apps. This could be

because majority of the sample population consisted of people in the age group 20 to

24 (69.2%).

Thus the hypothesis based on literature review which stated “Age group had an

impact on Behavioral intention towards use of recruitment apps” is not supported.

42

Chapter 5: Limitations of the Study

The following are the limitations for the study:

1. Data collection was confined to some colleges in Kerala.

2. The size of the sample is small

3. The time frame for the project is less

4. All variables of TAM model has not been taken into consideration.

42

Chapter 6: Findings and Suggestions

Out of the total respondents 33% of them had used recruitment apps and 66%

of them had not used recruitment apps. This means that only a few of the respondents

have adopted to the technology of recruitment apps and there is a high level of

reluctances with respect to use of recruitment apps.

Majority of the respondents (58.8 %) had come to know about recruitment apps

via friends. 23.5% became aware of recruitment apps while simply searching through

app store, 11.8 % became aware through many other sources like google search for

websites. 5.9% of them became aware about apps from their family / relatives.

59% of the 33% respondents who used recruitment apps had discontinued its

usage whereas about 41% still used apps.

Majority of the respondents (41.2%) felt complexity and inability to customize

resume as the main reason as to why professionals have not applied to a job via

mobile device. 29.4 % of them had not been looking for it. 17.6 % felt the main

reluctance towards apps could be the inability of apps to store resume and the amount

of time consumption which was often too long than other means. 5.29% of them

needed more time to adjust with the use of apps. 5.90% couldn’t find any mobile

application availability. None of them felt uninteresting job could be reason for

reluctance for using recruitment apps.

Majority of the respondents (79.6 %) who had never used recruitment apps

where using internet in smartphones several times a day.

89.2% of the respondents used watsapp. 81.4% were using apps for social

media engagement and 88.2 were using it for other entertainment purposes. 77.5%

were using apps to read news. 42.2 % used apps for job search and 5.9 % used apps

for other purposes like playing games.

80% of respondents who never used recruitment apps believed that apps could

definitely help in job search while 20% of them felt apps could never be helpful with

regard to search of job opportunities.

43

4.9% of the respondents strongly believed that a company should be able to

show its mettle in mobile applications. 18.4 % just agreed to the statement. 53.4 %

were in a neutral opinion meaning its fine both ways. 7.8% strongly disagreed to the

statement and 15.5 % could not support the statement.

4.9% of the respondents strongly believed that a company should not waste its

time building complicated apps. 30.1 % just supported the statement. 33 % were in a

neutral opinion meaning it is fine both ways. 23.3% could not support the statement

and 8.7 % totally disagreed believing apps were not waste of time and money.

88.2 % of respondents wanted recruitment apps to give them job alerts. 80.4 %

felt they should be able to apply for jobs via recruitment apps. 76.5 % wanted to these

apps to enable them to track their application status. 58.8 % felt they should be able to

share information via email also. 36.3 % felt recruitment apps should enable them to

share information via social networks as well. 2% wanted other features such as

history of the company and recent developments in the company as well.

There is a significant level of correlation among the variables.

From the results of SEM it is clear that Attitude towards use of recruitment apps is

more influenced by users perceived usefulness (β = 0.41) than their Perceived ease of

usage ( β = 0.24). Behavioural Intension to use of recruitment apps is most influenced

by Attitude towards use (β = 0.40), followed by perceived ease of usage (β = 0.30)

and Perceived usefulness (β = 0.24).

Since Significance value is greater than 0.01 for the influence of gender aspect,

this implied gender had no effect on Behavioural intention to use of recruitment apps.

Since Significance value is greater than 0.01 for the influence of age group

aspect, this implied age group had no effect on Behavioural intention to use of

recruitment apps.

Respondents also had the following suggestions:

1. Awareness level of recruitment via apps is less. So people should be made

more aware by use of advertisements or posts in company website and social

media.

2. Apps must not be complex and should be made more user-friendly and ease to

use.

44

3. Job seekers expect career websites to be optimized for mobile. So career

websites should also be taken care.

4. Post open positions on sites with mobile apps

5. Enable creating an account for the first time also via apps, because many

company apps just help you login through apps only after an account has been

created in their website

6. Provide more information in apps

7. Enable tracking of application status easy.

8. Provide push notifications about latest job openings.

9. Add all features needed in recruitment process in company’s recruitment apps.

10. Provide all the necessary information about the recruitment procedure in apps.

46

Chapter 7: Conclusion

In general, this study modified the original TAM in order to measure

behavioural intention of recruitment apps. The study has adopted the core constructs

used in TAM. Specifically, it validates the relationship between perceived ease of use,

perceived usefulness, attitude towards usage, and overall impact on behavioural

intention to use of recruitment apps. The study revealed that neither age nor gender

would influence behavioural intension to use of recruitment apps. Since the study was

conducted among users as well as non – users of recruitment apps, so the study could

identify what are the problems that users faced as well as addition of what all features

would enable non – users to become users. Since the study was confined to Kerala

state alone this study just brings out the Keralites intention to use of recruitment apps

alone.

47

Chapter 8: References

1. W. H. Dutton and B. D. Loader, Digital academe: new media in higher education

and learning: Routledge, 2004.

2. D. Radcliffe, "Technological and pedagogical convergence between work-based

and campus-based learning," Educational Technology & Society, vol. 5, pp. p54-

59, 2002.

3. S. Naidu, E-learning: A guidebook of principles, procedures and practices:

Commonwealth Educational Media Centre for Asia (CEMCA), 2003.

4. K. A. Al-Busaidi and H. Al-Shihi, "Instructors' Acceptance of Learning

Management Systems: A Theoretical Framework," Communications of the

IBIMA, vol. 2010, p. 2010, 2010.

5. M. F. Paulsen, "Experiences with Learning Management Systems in 113

European Institutions," Educational Technology & Society, vol. 6, pp. 134-148,

2003.

6. M. F. Paulsen, "Online Education Systems: Discussion and definition of terms,"

NKI Distance Education, 2002.

7. N. Cavus and A. a. M. Momani, "Computer aided evaluation of learning

management systems," Procedia - Social and Behavioral Sciences, vol. 1, pp.

426-430, 2009.

8. P. Arroway, E. Davenport, G. Xu, and D. Updegrove, "EDUCAUSE core data

service fiscal year 2009 summary report," Boulder, CO: EDUCAUSE, 2010.

9. T. Browne, M. Jenkins, and R. Walker, "A longitudinal perspective regarding the

use of VLEs by higher education institutions in the United Kingdom," Interactive

Learning Environments, vol. 14, pp. 177-192, 2006.

10. M. Robinson and M. Ally, "Transition to e-Learning in a Gulf Arab Country," in

The 2nd Annual Forum on e-Learning Excellence in the Middle East, Dubai,

UAE, 2009.

48

Appendix

Questionnaire

Three sets of questionnaires were used for the study. The first set enquired

about the basic demographic details of all the respondents. The second set was for non

– users of recruitment apps. The third set was for users of recruitment apps.

Questionnaire Section – I

Gender

1. Male

2. Female

Age

1. Below 20 years

2. 20 to 24 years

3. 25 to 30 years

4. Above 30 years

Undergraduation

1. Btech

2. BCA

3. BSC

4. Others

Questionnaire Section – II – for Non – users

Do you have a smart phone?

1. Yes

2. No

How often do you use internet in your phone?

1. Several times a day

2. 1 to 2 times a day

3. 3 to 5 days a week

49

4. 1 to 2 days a week

5. 1 to 3 times a month

6. Only when I feel it’s absolutely necessary

7. Never

For what purposes do you use apps?

1. Reading News

2. Job search

3. Entertainment

4. Social media

5. Watsapp

6. Other

Do you feel apps can help you in Job search?

1. Yes 1.

2. No 2.

“I have stopped applying if their mobile technology isn’t up to par. That alone tells

you something about the company’s priorities and whether they are savvy.” [Put

tick on the option of your choice]

1. Strongly Disagree

2. Disagree 1.

3. Neutral 2.

4. Strongly Agree 3.

5. Agree 4.

50

“I haven’t downloaded my dream company’s career app on my mobile device.

However, I have visited my dream company’s career site. Save time and money and

invest in your site instead of building a complicated app”. [Put tick on the option of

your choice]

1. Strongly Disagree

2. Disagree

3. Neutral

4. Strongly Agree

5. Agree

What are your expectations from your dream company’s recruitment app?

1. Should be able to receive job alerts

2. Should be able to track application status

3. Should be able to apply for jobs

4. Should be able to share information via email

5. Should be able to share information through social networks

6. Other

Questionnaire Section – II – for Users of recruitment apps

How did you come to know about the company’s recruitment app?

1. Friends

2. Family / Relatives

3. While simply searching through app store

4. Other

51

Are you still using the app?

1. Yes 1.

2. No 2.

What according to you could be the reason as to why professionals have not applied

to a job via mobile device?

1. No resume stored

2. Needed more time to adjust with the use of apps

3. Haven’t been looking

4. No interesting job

5. Couldn’t customize resume

6. No mobile apply option

7. Tried but took too long

8. Tried but too complex

9. Other

Questions with respect to TAM Model

Answer the following Questions based on a recruitment app of your choice

Name of the recruitment app Chosen :

Perceived Usefulness

1. Rate your favourite company recruitment app (from 1 : Worst to 5 : Best)

based on its Content

2. Rate your favourite company recruitment app (from 1 : Worst to 5 : Best)

based on its Performance.

Perceived Ease of Usage

1. Rate your favourite company recruitment app (from 1 : Worst to 5 : Best)

based on its Content

2. Rate your favourite company recruitment app (from 1 : Worst to 5 : Best)

based on its Performance.

52

3. Rate your favourite company recruitment app (from 1 : Worst to 5 : Best)

based on its capability to navigate from one content to another.

4. Rate your favourite company recruitment app (from 1 : Worst to 5 : Best)

based on its Ease of use.

Attitude towards Use of recruitment apps

1. I like using the app

a) Strongly Disagree

b) Disagree

c) Neutral

d) Strongly Agree

e) Agree

2. In my Opinion it is desirable to use recruitment apps

a) Strongly Disagree

b) Disagree

c) Neutral

d) Strongly Agree

e) Agree

Behavioural Intension to use of recruitment apps

1. I Plan to use recruitment apps in future

a) Strongly Disagree

b) Disagree

c) Neutral

d) Strongly Agree

e) Agree

2. Assuming that I have access to it, I intend to use it

a) Strongly Disagree

53

b) Disagree

c) Neutral

d) Strongly Agree

e) Agree